Magnetic resonance imaging features for differentiating tuberculous from pyogenic spondylitis: a meta-analysis.

OBJECTIVE: To perform a meta-analysis comparing the MRI features of tuberculous and pyogenic spondylitis, using histopathological results and/or blood culture as the standard reference. MATERIALS AND METHODS: PubMed, Embase, Web of Science, and Cochrane Library were searched for English-language studies on the MRI features of tuberculous and pyogenic spondylitis published between January 2010 and February 2023. Risk for bias and concerns regarding applicability were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Pooled MRI features' proportions were calculated using a bivariate random-effects model. RESULTS: Thirty-two studies met the inclusion criteria: 21 for tuberculous spondylitis, three for pyogenic spondylitis, and eight for both. Of the nine informative MRI features comparing tuberculous spondylitis to pyogenic spondylitis, involvement of ≥ 2 vertebral bodies (92% vs. 88%, P = .004), epidural extension (77% vs. 25%, P < .001), paravertebral collection (91% vs. 84%, P < .001), subligamentous spread (93% vs. 24%, P < .001), thin and regular abscess wall (94% vs. 18%, P < .001), vertebral collapse (68% vs. 24%, P < .001), and kyphosis (39% vs. 3%, P < .01) were more suggestive of tuberculous spondylitis, while disc signal change (82% vs. 95%, P < .001) and disc height loss (22% vs. 59%, P < .001) were more suggestive of pyogenic spondylitis. CONCLUSION: Involvement of ≥ 2 vertebral vertebral bodies, soft tissue attribution, thin and regular abscess wall, vertebral collapse, and kyphosis were MRI features more common in tuberculous spondylitis, while disc signal change and height loss were more common in pyogenic spondylitis.

Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA.

N4-acetylcytidine (ac4C) is a modification of cytidine at the nitrogen-4 position, playing a significant role in the translation process of mRNA. However, the precise mechanism and details of how ac4C modifies translated mRNA remain unclear. Since identifying ac4C sites using conventional experimental methods is both labor-intensive and time-consuming, there is an urgent need for a method that can promptly recognize ac4C sites. In this paper, we propose a comprehensive ensemble learning model, the Stacking-based heterogeneous integrated ac4C model, engineered explicitly to identify ac4C sites. This innovative model integrates three distinct feature extraction methodologies: Kmer, electron-ion interaction pseudo-potential values (PseEIIP), and pseudo-K-tuple nucleotide composition (PseKNC). The model also incorporates the robust Cluster Centroids algorithm to enhance its performance in dealing with imbalanced data and alleviate underfitting issues. Our independent testing experiments indicate that our proposed model improves the Mcc by 15.61% and the ROC by 5.97% compared to existing models. To test our model's adaptability, we also utilized a balanced dataset assembled by the authors of iRNA-ac4C. Our model showed an increase in Sn of 4.1%, an increase in Acc of nearly 1%, and ROC improvement of 0.35% on this balanced dataset. The code for our model is freely accessible at https://github.com/louliliang/ST-ac4C.git, allowing users to quickly build their model without dealing with complicated mathematical equations.

Integrative approach for classifying male tumors based on DNA methylation 450K data.

Malignancies such as bladder urothelial carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, lung adenocarcinoma and prostate adenocarcinoma significantly impact men's well-being. Accurate cancer classification is vital in determining treatment strategies and improving patient prognosis. This study introduced an innovative method that utilizes gene selection from high-dimensional datasets to enhance the performance of the male tumor classification algorithm. The method assesses the reliability of DNA methylation data to distinguish the five most prevalent types of male cancers from normal tissues by employing DNA methylation 450K data obtained from The Cancer Genome Atlas (TCGA) database. First, the chi-square test is used for dimensionality reduction and second, L1 penalized logistic regression is used for feature selection. Furthermore, the stacking ensemble learning technique was employed to integrate seven common multiclassification models. Experimental results demonstrated that the ensemble learning model utilizing multiple classification models outperformed any base classification model. The proposed ensemble model achieved an astonishing overall accuracy (ACC) of 99.2% in independent testing data. Moreover, it may present novel ideas and pathways for the early detection and treatment of future diseases.

Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction.

Accurate identification of inter-chain contacts in the protein complex is critical to determine the corresponding 3D structures and understand the biological functions. We proposed a new deep learning method, ICCPred, to deduce the inter-chain contacts from the amino acid sequences of the protein complex. This pipeline was built on the designed deep residual network architecture, integrating the pre-trained language model with three multiple sequence alignments (MSAs) from different biological views. Experimental results on 709 non-redundant benchmarking protein complexes showed that the proposed ICCPred significantly increased inter-chain contact prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the significant advantage of ICCPred lies in the utilization of pre-trained transformer language models which can effectively extract the complementary co-evolution diversity from three MSAs. Meanwhile, the designed deep residual network enhances the correlation between the co-evolution diversity and the patterns of inter-chain contacts. These results demonstrated a new avenue for high-accuracy deep-learning inter-chain contact prediction that is applicable to large-scale protein-protein interaction annotations from sequence alone.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases.

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model.

As one of the most common diseases in pediatric surgery, an inguinal hernia is usually diagnosed by medical experts based on clinical data collected from magnetic resonance imaging (MRI), computed tomography (CT), or B-ultrasound. The parameters of blood routine examination, such as white blood cell count and platelet count, are often used as diagnostic indicators of intestinal necrosis. Based on the medical numerical data on blood routine examination parameters and liver and kidney function parameters, this paper used machine learning algorithm to assist the diagnosis of intestinal necrosis in children with inguinal hernia before operation. In the work, we used clinical data consisting of 3,807 children with inguinal hernia symptoms and 170 children with intestinal necrosis and perforation caused by the disease. Three different models were constructed according to the blood routine examination and liver and kidney function. Some missing values were replaced by using the RIN-3M (median, mean, or mode region random interpolation) method according to the actual necessity, and the ensemble learning based on the voting principle was used to deal with the imbalanced datasets. The model trained after feature selection yielded satisfactory results with an accuracy of 86.43%, sensitivity of 84.34%, specificity of 96.89%, and AUC value of 0.91. Therefore, the proposed methods may be a potential idea for auxiliary diagnosis of inguinal hernia in children.

Drug resistance and genomic characteristics of Salmonella enterica serovar London from clinical and food sources in Hangzhou City from 2017 to 2021 / 中华预防医学杂志

Objective: To analyze the drug resistance and genomic characteristics of Salmonella enterica serovar London isolated from clinical and food sources in Hangzhou City from 2017 to 2021. Methods: A total of 91 Salmonella enterica serovar London strains isolated from Hangzhou City from 2017 to 2021 were analyzed for drug susceptibility, pulsed field gel electrophoresis (PFGE) typing and whole genome sequencing. Multilocus sequence typing (MLST), core genome multilocus sequence typing (cgMLST) and detection of drug resistance genes were performed by using the sequencing data. Phylogenetic analysis was conducted to compare the 91 genomes from Hangzhou City with 347 genomes from public databases. Results: No significant difference in the drug resistance rate was observed between clinical strains and food strains to 18 drugs in Hangzhou City(all P>0.05), and the multidrug resistance (MDR) rate was 75.8% (69/91). Most strains were resistant to 7 drug classes simultaneously. One strain was resistant to Polymyxin E as well as positive for mcr-1.1, and 50.5% (46/91) of the strains were resistant to Azithromycin and were positive for mph(A). All 91 Salmonella enterica serovar London strains were ST155, which were subdivided into 44 molecular types by PFGE and 82 types by cgMLST. Phylogenetic analysis showed that most strains from Hangzhou City (83/91) were clustered together, and a small number of human isolates from Europe, North America and pork isolates from Hubei and Shenzhen were mixed in the cluster. Other strains from Hangzhou City (8/91) were closely related to strains from Europe, America and Southeast Asia. Strains isolated from pork were the most closely related to clinical strains. Conclusion: The epidemic of Salmonella enterica serovar London in Hangzhou City is mainly caused by the spread of ST155 strains, which is mainly transmitted locally. At the same time, cross-region transmission to Europe, North America, Southeast Asia, and other provinces and cities in China may also occur. There is no significant difference in the drug resistance rate between clinical strains and food strains, and a high level of MDR is found in the strains. Clinical infection of Salmonella enterica serovar London may be closely related to pork consumption in Hangzhou City.

Genetic analysis of a Chinese pedigree with 6q26q27 microduplication and 15q26.3 microdeletion / 中华医学遗传学杂志

OBJECTIVE@#To explore the genetic basis for a Chinese pedigree with 6q26q27 microduplication and 15q26.3 microdeletion.@*METHODS@#A fetus with a 6q26q27 microduplication and a 15q26.3 microdeletion diagnosed at the First Affiliated Hospital of Wenzhou Medical University in January 2021 and members of its pedigree were selected as the study subject. Clinical data of the fetus was collected. The fetus and its parents were analyzed by G-banding karyotyping and chromosomal microarray analysis (CMA), and its maternal grandparents were also subjected to G-banding karyotype analysis.@*RESULTS@#Prenatal ultrasound had indicated intrauterine growth retardation of the fetus, though no karyotypic abnormality was found with the amniotic fluid sample and blood samples from its pedigree members. CMA revealed that the fetus has carried a 6.6 Mb microduplication in 6q26q27 and a 1.9 Mb microdeletion in 15q26.3, and his mother also carried a 6.49 duplication and a 1.867 deletion in the same region. No anomaly was found with its father.@*CONCLUSION@#The 6q26q27 microduplication and 15q26.3 microdeletion probably underlay the intrauterine growth retardation in this fetus.

Clinical phenotype and genetic analysis of a child with 3p26.3p25.3 deletion / 中华医学遗传学杂志

OBJECTIVE@#To explore the genetic basis for a child with facial dysmorphism and multiple malformations.@*METHODS@#The child, born at 34+6 weeks' gestation due to premature rupture of amniotic membrane, dichorionic diamniotic twinning and gestational diabetes, was subjected to chromosomal karyotyping analysis and copy number variations sequencing (CNV-seq).@*RESULTS@#The child was found to have facial dysmorphism, hypospadia, cryptorchidism and hypotonia. He was found to have a 46,XY,del(3)(p26) karyotype in addition with a 9.80 Mb deletion (chr3: 60 000-9 860 000) encompassing 33 protein coding genes.@*CONCLUSION@#The 3p26.3p25.3 deletion probably underlay the multiple malformations in this child. Continuous follow-up is required to improve his quality of life.

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data.

The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.

Drug-target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods.

Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew's correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.

Research on anthracnose grade of Camellia oleifera based on the combined LIBS and THz technology.

BACKGROUND: Anthracnose of Camellia oleifera is a very destructive disease that commonly occurs in the Camellia oleifera industry, which severely restricts the development of the Camellia oleifera industry. In the early stage of the Camellia oleifera suffering from anthracnose, only the diseased parts of the tree need to be repaired in time. With the aggravation of the disease, the diseased branches need to be eradicated, and severely diseased plants should be cut down in time. At present, aiming at the problems of complex experiments and low accuracy in detecting the degree of anthracnose of Camellia oleifera, a method is proposed to detect the degree of anthracnose of Camellia oleifera leaves by using terahertz spectroscopy (THz) combined with laser-induced breakdown spectroscopy (LIBS), so as to realize the rapid, efficient, non-destructive and high-precision determination of the degree of anthracnose of Camellia oleifera. RESULTS: Mn, Ca, Ca II, Fe and other elements in the LIBS spectrum of healthy and infected Camellia oleifera leaves with different degrees of anthracnose are significantly different, and the Terahertz absorption spectra of healthy Camellia oleifera leaves, and Camellia oleifera leaves with different degrees of anthracnose there are also significant differences. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and linear discriminant analysis (LDA) are used to establish the fusion spectrum anthracnose classification model of Camellia oleifera. Among them, the Root mean square error of prediction (RMSEP) and the prediction determination coefficient R2p of THz-LIBS-CARS-PLS-DA of prediction set are 0.110 and 0.995 respectively, and the misjudgment rate is 1.03%; The accuracy of the modeling set of THz (CARS)-LIBS (CARS)-SVM is 100%, and the accuracy of prediction set is 100%, after preprocessing of the multivariate scattering correction (MSC), the accuracy of the THz-LIBS-MSC-CARS modeling set is 100%, and the accuracy of prediction set is 100%; The accuracy rate of THz-LIBS-MSC-CARS-LDA of modeling set is 98.98%, and the accuracy rate of the prediction set is 96.87%. CONCLUSION: The experimental results show that: the SVM model has higher qualitative analysis accuracy and is more stable than the PLS-DA and LDA models. The results showed that: the THz spectrum combined with the LIBS spectrum could be used to separate healthy Camellia oleifera leaves from various grades of anthracnose Camellia oleifera leaves non-destructively, quickly and accurately.

The Prognosis of Skip Metastasis in Papillary Thyroid Microcarcinoma Is Better Than That of Continuous Metastasis.

CONTEXT: A few papillary thyroid microcarcinomas (PTMCs) may have skip metastasis (SLNM), but the risk factors remain controversial and the prognosis is unclear. OBJECTIVES: To investigate the incidence, lymph node metastasis (LNM) patterns, risk factors, and prognosis of SLNM in PTMCs. METHODS: We reviewed the medical records of PTMC patients who underwent thyroid surgery in our institution. Analyses of risk factors were performed for SLNM. Recurrence-free survival (RFS) of SLNM, central lymph node metastasis (CLNM), and continuous metastasis (CLNM and lateral lymph node metastasis [CLNM + LLNM]) were compared after propensity score matching (PSM). RESULTS: SLNM was detected in 1.7% (50/3923) and frequently involved level III (66.7%). Compared with CLNM + LLNM, SLNM had more LNM at a single level (P < 0.01) and less LNM at 2 levels (P < 0.05). A tumor size of 0.5 to 1 cm (odds ratio [OR], 2.26; 95% CI, 1.27-4.00) and location in the upper pole (OR, 3.30; 95% CI, 2.02-5.40) were independent risk factors for SLNM. A total of 910 (23.2%) PTMCs with LNM were included in the prognostic analysis. At a median follow-up of 60 months, the RFS of SLNM did not differ from that of CLNM (P = 0.10) but was significantly higher than that of CLNM + LLNM (P < 0.01) after using PSM. CONCLUSIONS: When the tumor size is 0.5 to 1 cm or its location is in the upper pole, we must remain vigilant to SLNM in PTMC. Because its prognosis is comparable to that of only CLNM and better than that of CLNM + LLNM, less intensive treatment should be considered.

Computed Tomography Features of Adnexal Torsion: A Meta-Analysis.

OBJECTIVES: We performed a meta-analysis of studies examining the computed tomography (CT) features of adnexal torsion (AT). METHODS: We searched PubMed, Embase, Web of Science, and the Cochrane Library for studies involving the proportion of CT features in patients with AT and that used surgery as the reference test. Study quality was assessed using the Newcastle-Ottawa scale and the Agency for Healthcare Research and Quality tool. RESULTS: Twelve articles involving 483 patients were included. The pooled proportion of right-sided adnexal lesion was 54% (95% confidence interval [CI]: 49%-56%). The pooled proportions of the ovarian lesion histopathological types were: benign germ cell tumors, 33% (95% CI: 28%-37%); benign cystic lesions, 26% (95% CI: 21%-30%); benign epithelial neoplasms, 24% (95% CI: 20%-29%); sex cord-stromal tumors, 4% (95% CI: 2%-6%); borderline neoplasms, 3% (95% CI: 1%-6%); and hemorrhagic cysts, 2% (95% CI: 0%-3%). The pooled proportions of CT features were: Adnexal enlargement, 99% (95% CI: 98%-99%); adnexal with mass, 98% (95% CI: 97%-100%); twisted pedicle, 81% (95% CI: 78%-83%); mass with thickened wall, 77% (95% CI: 73%-81%); tubal thickening, 73% (95% CI: 68%-77%); abnormal location of adnexa, 69% (95% CI: 63%-75%), pelvic ascites, 43% (95% CI: 38%-49%); pelvic fat infiltration, 41% (95% CI: 34%-48%); uterine deviation, 37% (95% CI: 31%-42%); and lack of enhancement, 20% (95% CI: 14%-25%). CONCLUSION: Adnexal enlargement, adnexal mass, and twisted pedicle may be the most important CT features for diagnosing AT.

Long-term outcomes and prognostic factors in papillary thyroid microcarcinoma patients with distant metastases.

PURPOSES: Distant metastasis from papillary thyroid microcarcinoma (PTMC) is extremely rare and the long-term outcomes and independent prognostic factors remain unclear. The present study aimed to investigate clinicopathological characteristics and evaluate the long-term outcomes and prognostic factors of PTMC patients with distant metastases (DM) who underwent surgery and radioactive iodine (131I) treatment. METHODS: We retrospectively reviewed the medical records of 13,441 patients with thyroid cancer (including 1697 cases with PTMC) who underwent 131I treatment at our institution between January 2008 and December 2019. PTMC patients with distant metastases with sufficient clinical follow-up data were enrolled in this cohort study. The overall survival (OS) and progression-free survival (PFS) were analyzed by the Kaplan-Meier method and the prognostic factors were assessed by Cox proportional hazards. RESULTS: Thirty-three PTMC patients with DM were enrolled in this study. The median follow-up was 75 months (range: 5-151 months). The 5-year and 10-year OS rates were 96.97 and 81.41%, respectively, and the 5-year and 10-year PFS rates were 90.46 and 69.68%, respectively. Multivariate analysis showed that male sex (P = 0.005), radioactive iodine refractory PTMC (P = 0.033), and symptomatic DM (P = 0.022) were significantly associated with worse 10-year PFS in PTMC patients with DM. No independent predictor related to poor 10-year OS was found in the present study. CONCLUSIONS: The prognosis of PTMC patients becomes worse after the development of DM. Male sex, radioactive iodine refractory PTMC, and symptomatic DM were identified as independent factors associated with PFS.

One-year follow-up results of atrial fibrillation patients who undergoing transcatheter aortic valve implantation / 中华心血管病杂志

Objective: To investigate whether atrial fibrillation (AF) before transcatheter aortic valve implantation (TAVI) will affect the prognosis of patients post TAVI. Methods: This is a single center retrospective study. A total of 115 patients with severe aortic stenosis (AS) who were admitted to General Hospital of Northern Theater Command from May 2016 to November 2020 and successfully received TAVI treatment were included. According to absence or accompaniment of AF pre-TAVI, they were divided into AF group (21 cases) and non-AF group (94 cases). The patients were followed up for postoperative antithrombotic treatment and the occurrence of the net adverse clinical and cerebrovascular events (NACCE) at 12 months post TAVI, including cardiogenic death, readmission to hospital for heart failure, nonfatal myocardial infarction, ischemic stroke and severe bleeding (BARC levels 3-5). Univariate logistic regression was used to analyze the related factors of NACCE. Results: Among the 115 selected patients, age was (73.8±6.9) years, there were 63 males. And 21 cases (18.2%) were diagnosed as AFbefore TAVI. In terms of postoperative antithrombotic therapy, 48.9% (46/94) of the patients in the non-AF group received monotherapy and 47.9% (45/94) received dual antiplatelet therapy. In the AF group, 47.6% (10/21) received anticoagulants and 33.3% (7/21) received dual antiplatelet therapy. The proportion of patients in the AF group taking non-vitamin K antagonist oral anticoagulants (NOAC) was higher than that in the non-AF group (38.1% (8/21) vs. 2.1% (2/94), P<0.001). Patients in both groups were followed up to 12 months after TAVI. During the 12 months follow-up, the incidence of NACCE after TAVI was 14.3% (3/21) in the AF group, which was numerically higher than that in the non-AF group (6.4% (6/94)), but the difference was not statistically significant (P=0.441). The incidence of severe bleeding was significantly higher in the AF group than in the non-AF group (9.5% (2/21) vs. 0, P=0.032). Univariate logistic regression analysis showed that hypertension was associated with the risk of NACCE (OR=8.308, P=0.050), while AF was not associated with the risk of NACCE (P=0.235). Conclusion: The incidence of severe bleeding after TAVI is higher in patients with AF than in patients without AF prior TAVI, and there is a trend of increased risk of NACCE post TAVI in AF patients.

Impact of probiotics on the lung development of Bama minipig after premature birth / 中华儿科杂志

Objectives: To examine the impact of probiotics on the lung development of preterm birth of Bama pig. Methods: From April 2020 to October 2021, this animal experimental research was performed by setting up preterm (birth at gestation 104 d), full-term (birth at gestation 113 d), preterm with probiotics (birth at gestation 104 d treated with probiotics given at 3 d after birth), and full-term with probiotics (birth at gestation 113 d treated with probiotics given at 3 d after birth) groups and using the preterm Bama minipig model, the body weights were recorded and lung, ileum, and intestinal content samples were collected at birth, 4 days, 9 days, and 21 days after births of the piglets in preterm and full-term groups, the same samples were collected on 9 days after births of the piglets in preterm with probiotics and full-term with probiotics groups. The body weight and radial alveolar counts (RAC) were compared to evaluate the lung development of the piglets. The lengths of ileal villus were compared to evaluate the development of ileum. The composition structures of bacteria in ileum were analyzed by 16 S rRNA sequencing. The statistical analyses between different groups were performed by t test. Results: There were totally 30 piglets (16 female piglets and 14 male piglets) involving 12 piglets in preterm and full-term groups respectively and 3 piglets in preterm with probiotics and full-term with probiotics groups respectively. The body weights of the piglets in preterm group were lower than those in full-term group at 4, 9 and 21 d after birth ((507±27) vs. (694±56) g, (620±35) vs. (1 092±154) g, (1 660±210) vs. (2 960±418) g,t=2.96, 2.99, 2.78, all P<0.05). The alveolarization of the preterm piglets at 9 days after birth was significantly lower than that of the full-term piglets at the equivalent time point (4.00±0.29 vs. 6.11±0.35, t=4.64, P<0.01). The bacteria genus with the highest abundance in ileum were all different between the preterm and the full-term groups at 4, 9 and 21 d after birth (4 d Escherichia-Shigella (26.63%) and Enterococcus (30.48%) respectively;9 d Turicibacter (35.94%) and Lactobacillus (27.33%) respectively;21 d Escherichia-Shigella (28.02%) and Lactobacillus (46.29%) respectively). The heights of ileal villus of the preterm piglets at 9 d after birth were significantly lower than those of the full-term minipigs at the equivalent time point ((297±21) vs. (411±32) μm, t=3.01, P=0.007).There were both no differences in the body weight and alveolarization ((692±36) vs. (767±67) g, 5.44±0.34 vs. 5.89±0.26, t=0.74, 1.04, both P>0.05) between the piglets in preterm with probiotics group and those in full-term with probiotics group. Turicibacter was the dominant genus in the piglets of both preterm with probiotics and the full-term with probiotics groups. The heights of ileal villus of the piglets in preterm with probiotics group were significantly longer that those of the piglets in preterm group ((371±13) vs. (297±21) μm, t=3.04, P=0.006), and were both not significantly different from those of the piglets in full-term with probiotics group and full-term group ((371±13) vs. (338±12) and (411±32) μm, t=1.90, 1.15, both P>0.05). Conclusions: Premature birth could impact the lung alveolarization of piglets. The probiotics could improve the lung alveolarization of preterm minipigs by promoting the development of ileum.

Predicting S-nitrosylation proteins and sites by fusing multiple features.

Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of k-spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS.

Construction of a Self-Assembled Polyelectrolyte/Graphene Oxide Multilayer Film and Its Interaction with Metal Ions.

In this study, a composite multilayer film onto gold was constructed from two charged building blocks, i.e., negatively charged graphene oxide (GO) and a branched polycation (polyethylenimine, PEI) via layer-by-layer (LbL) self-assembly technology, and this process was monitored in situ with quartz crystal microbalance (QCM) under different experimental conditions. This included the differences in frequency (Δf) as well as the changes in dissipation to yield information on the absorbed mass and viscoelastic properties of the formed PEI/GO multilayer films. The experimental conditions were optimized to obtain a high amount of the adsorbed mass of the self-assembled multilayer film. The surface morphology of the PEI/GO multilayer film onto gold was studied with atomic force microscopy (AFM). It was found that the positively charged PEI chains were combined with the oppositely charged GO to form an assembled film on the QCM sensor surface, in a wrapped and curled fashion. Raman and UV-vis spectra also showed that the intensities of the GO-characteristic signals are almost linearly related to the layer number. To explore the films for their use in divalent ion detection, the frequency response of the PEI/GO multilayer-modified QCM sensor to the exposure of aqueous solutions solution of Cu2+, Ca2+, Zn2+, and Sn2+ was further studied using QCM. Based on the Sauerbrey equation and the weight of different ions, the number of metal ions adsorbed per unit area on the surface of QCM sensors was calculated. For metal ion concentrations of 40 ppm, the adsorption capacities per unit area of Cu2+, Zn2+, Sn2+, and Ca2+ were found to be 1.7, 3.2, 0.7, and 4.9 nmol/cm2, respectively. Thus, in terms of the number of adsorbed ions per unit area, the QCM sensor modified by PEI/GO multilayer film shows the largest adsorption capacity of Ca2+. This can be rationalized by the relative hydration energies.